GAN-based unpaired image-to-image translation for maritime imagery

Chelsea Mediavilla; Jonathan Sato; Mitch Manzanares; Marissa Dotter; Shibin Parameswaran

doi:10.1117/12.2561732

2 June 2020 GAN-based unpaired image-to-image translation for maritime imagery

Chelsea Mediavilla, Jonathan Sato, Mitch Manzanares, Marissa Dotter, Shibin Parameswaran

Proceedings Volume 11398, Geospatial Informatics X; 113980K (2020) https://doi.org/10.1117/12.2561732
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Generating imagery using gaming engines has become a popular method to both augment or completely replace the need for real data. This is due largely to the fact that gaming engines, such as Unity3D and Unreal, have the ability to produce novel scenes and ground-truth labels quickly and with low-cost. However, there is a disparity between rendering imagery in the digital domain and testing in the real domain on a deep learning task. This disparity/gap is commonly known as domain mismatch or domain shift, and without a solution, renders synthetic imagery impractical and ineffective for deep learning tasks. Recently, Generative Adversarial Networks (GANs) have shown success at generating novel imagery and overcoming this gap between two different distributions by performing cross-domain transfer. In this research, we explore the use of state-of-the-art GANs to perform a domain transfer between a rendered synthetic domain to a real domain. We evaluate the data generated using an image-to-image translation GAN on a classification task as well as by qualitative analysis.

Conference Presentation

Citation Download Citation

Chelsea Mediavilla, Jonathan Sato, Mitch Manzanares, Marissa Dotter, and Shibin Parameswaran "GAN-based unpaired image-to-image translation for maritime imagery", Proc. SPIE 11398, Geospatial Informatics X, 113980K (2 June 2020); https://doi.org/10.1117/12.2561732

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